A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
This report has been generated by the nf-core/aquascope analysis pipeline. For information about how to interpret these results, please see the documentation.
Report
generated on 2024-11-02, 18:21 EDT
based on data in:
/scicomp/scratch/xuz1/nextflow/work/1c/7c1e1acdc9a39ca2cc5c82fd48b384
General Statistics
| Sample Name | % GC | ≥ 30X | Median cov | Mean cov | % On target | % Aligned | Top lineage | Top lineage % | % Duplication | M Reads After Filtering | GC content | % PF |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sample1 | 39% | 95.2% | 1766.0X | 1854.8X | 100.0% | 100.0% | Omicron | 99.7% | 0.2% | 0.1M | 38.5% | 92.4% |
| Sample2 | 39% | 93.1% | 1720.0X | 1812.5X | 100.0% | 100.0% | Other | 100.0% | 0.0% | 0.1M | 38.4% | 92.0% |
CDCGov-Aquascope Workflow Summary
- this information is collected when the pipeline is started.
Core Nextflow options
- runName
- trusting_euclid
- containerEngine
- singularity
- launchDir
- /scicomp/home-pure/xuz1/projects/aqua/aquascope
- workDir
- /scicomp/scratch/xuz1/nextflow/work
- projectDir
- /scicomp/home-pure/xuz1/projects/aqua/aquascope
- userName
- xuz1
- profile
- singularity,test_ont
- configFiles
- N/A
Input/output options
- input
- /scicomp/home-pure/xuz1/projects/aqua/aquascope/assets/samplesheet_test_ont.csv
- outdir
- ~/projects/tmp/aqua//ont
Institutional config options
- config_profile_name
- Test Long read profile
- config_profile_description
- Minimal test dataset to check pipeline function
Max job request options
- max_cpus
- 2
- max_memory
- 6 GB
- max_time
- 6h
Other parameters
- gff3
- /scicomp/home-pure/xuz1/projects/aqua/aquascope/assests/references/SARS-CoV-2.reference.gff3
- hostnames
- {}
NWSS/aquascope Methods Description
Suggested text and references to use when describing pipeline usage within the methods section of a publication.
Methods
Data was processed using NWSS/aquascope v3.0 (doi: ) of the nf-core collection of workflows (Ewels et al., 2020).
The pipeline was executed with Nextflow v24.04.2 (Di Tommaso et al., 2017) with the following command:
nextflow run main.nf --resume -profile singularity,test_ont -entry AQUASCOPE --outdir ~/projects/tmp/aqua//ontReferences
- Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. https://doi.org/10.1038/nbt.3820
- Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. https://doi.org/10.1038/s41587-020-0439-x
Notes:
- The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
- You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.
QualiMap
QualiMap is a platform-independent application to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.DOI: 10.1093/bioinformatics/btv566; 10.1093/bioinformatics/bts503.
Coverage histogram
Distribution of the number of locations in the reference genome with a given depth of coverage.
For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).
Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.
If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).
This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).
Cumulative genome coverage
Percentage of the reference genome with at least the given depth of coverage.
For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).
Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).
For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.
GC content distribution
Each solid line represents the distribution of GC content of mapped reads for a given sample.
GC bias is the difference between the guanine-cytosine content (GC-content) of a set of sequencing reads and the GC-content of the DNA or RNA in the original sample. It is a well-known issue with sequencing systems, and may be introduced by PCR amplification, among other factors (Benjamini & Speed 2012; Ross et al. 2013).
QualiMap calculates the GC-content of individual mapped reads, then groups those reads by their GC-content (1%, 2%, …, 100%), and plots the frequency of mapped reads (y-axis) at each level of GC-content (x-axis). This plot shows the GC-content distribution of mapped reads for each read dataset, which should ideally resemble that of the original sample. It can be useful to display the GC-content distribution of an appropriate reference sequence for comparison, and QualiMap has an option to do this (see the Qualimap 2 documentation).
Freyja
Freyja Recover relative lineage abundances from mixed SARS-CoV-2 samples.DOI: 10.1038/s41586-022-05049-6.
Freyja Summary
Relative lineage abundances from mixed SARS-CoV-2 samples. Hover over the column headers for descriptions and click Help for more in-depth documentation.
The graph denotes a sum of all lineage abundances in a particular WHO designation , otherwise they are grouped into "Other". Lineages abundances are calculated as the number of reads that are assigned to a particular lineage. Lineages and their corresponding abundances are summarized by constellation.
Note: Lineage designation is based on the used WHO nomenclature, which could vary over time.
fastp
0.23.4
fastp An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).DOI: 10.1093/bioinformatics/bty560.
Filtered Reads
Filtering statistics of sampled reads.
Sequence Quality
Average sequencing quality over each base of all reads.
GC Content
Average GC content over each base of all reads.
N content
Average N content over each base of all reads.
Software Versions
Software Versions lists versions of software tools extracted from file contents.
| Software | Version |
|---|---|
| fastp | 0.23.4 |
